Does too much liquidity generate instability?

Abstract

Corporate demand for cash is related to a number of firm-specific characteristics, like the presence of transaction costs, information asymmetry in credit markets, uncertainty and risk aversion. The purpose of this paper is to build a dynamic model that describes the potential chaotic effects of the accumulation of cash by firms over a prolonged period of time. By exploring the theoretical connections between firm financial policies and investment decisions, we show that too much liquidity might generate economic instability. When firm increases the share of cash devoted to risky investment, and reduces the share of cash distributed to shareholders as dividends, the fixed point of the system changes from being stable to being unstable. Moreover, the impact of such a policy on the stability of the system is larger the greater the investment risk. The chaotic behavior is mainly observable in the dynamics of cash, which in turn may affect all investment decisions.

Determinants of profitability in Chinese companies

“…The current ratio is used to measure company liquidity. This study reveals unexpected results regarding liquidity, which shows a significant but negative association between current ratio and EPS and ROA, which substantiates the view of Calcagnini et al (2020) and Bibi and Amjad (2017) but contradicts that of Pratheepan (2014) and Zaid et al. (2014).…”

Making Impact on Common Financial Factors Affecting Liquidity Ratio Based on Greedy Approach Through Price Movement in Stock Market

“…The consistency ratio should be less than or equal to 0.1, and it is vital to evaluate conclusions if the consistency ratio is more than or equal to 0.1. The character will have a Consistency index of 1.0 if the minimum number of steps is the same as the observed number of steps [18]. A character will have a Consistency index value of less than 1.0 if it is not entirely compatible with a Liquidity Market.…”

“…Thus it can be assured that the matched financial factors entertained the requirements of the liquidity factors. The user liquidity facts are matched with the actual financial factors based on four Degrees of the match: exact, plugin, subsumes, and fail [18]. [43][61] also available.…”

“…With minimum flexibility of match, the algorithm reduces false positives & increases false negatives, and with maximum flexibility, it increases false positives & reduces false negatives[18]. The algorithm's result also depends approximately on the liquidity factors concepts.…”

“…The bipartite Graph-based match-making approach overcomes the excess proceeds. Based on the Factors affecting (AF) the impact of a liquidity crisis, the Actual financial factors are got the following DOM values[18][44]. That is indicated in table7…”